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As scientific applications strive towards increasingly realistic modeling of complex phenomena, they are integrating multiple models and simulations into complex, coupled scientific workflows. As a result, ensuring that existing codes can be combined and recombined correctly and flexibly as part of these workflows is essential. In this paper, we propose Benesh, a programming system for creating in-situ scientific workflows. Benesh provides a domain-specific abstraction that enables a programmer to instrument an existing simulation code to be used as a building block in defining complex workflows. Using Benesh, developers define a workflow-level shared specification of data objects over common or partitioned data domains. This permits dependency-based execution to be specified at the workflow level, distinct from the independent operation of the component simulations. We additionally describe features of a scalable runtime that builds on a distributed data services layer to implement the Benesh programming system.

HPC applications use floating point arithmetic operations extensively to solve computational problems. Mixed-precision computing seeks to use the lowest precision data type that is sufficient to achieve a desired accuracy, improving performance and reducing power consumption. Manually optimizing a program to use mixed precision is challenging as it not only requires extensive knowledge about the numerical behavior of the algorithm but also estimates of the rounding errors. In this work, we present ADAPT, a scalable approach for mixed-precision analysis on HPC workloads using algorithmic differentiation to provide accurate estimates about the final output error. ADAPT provides a floating-point precision sensitivity profile while incurring an overhead of only a constant multiple of the original computation irrespective of the number of variables analyzed. The sensitivity profile can be used to make algorithmic choices and to develop mixed-precision configurations of a program. We evaluate ADAPT on six benchmarks and a proxy application (LULESH) and show that we are able to achieve a speedup of 1.2× on the proxy application.

Structured-grid PDE solver frameworks parallelize over boxes, which are rectangular domains of cells or faces in a structured grid. In the Chombo framework, the box sizes are typically 163 or 323, but larger box sizes such as 1283 would result in less surface area and therefore less storage, copying, and/or ghost cells communication overhead. Unfortunately, current on node parallelization schemes perform poorly for these larger box sizes. In this paper, we investigate 30 different inter-loop optimization strategies and demonstrate the parallel scaling advantages of some of these variants on NUMA multicore nodes. Shifted, fused, and communication-avoiding variants for 1283 boxes result in close to ideal parallel scaling and come close to matching the performance of 163 boxes on three different multicore systems for a benchmark that is a proxy for program idioms found in Computational Fluid Dynamic (CFD) codes.